The CIO’s strategic question is shifting from which application should we buy? to who or what should perform the work? In an opinion article published by CIO on October 6, 2026, Rajjie Sarmey proposes Enterprise Work Architecture (EWA): a way to redesign work across people, AI agents, applications, data, and delegated authority. EWA is Sarmey’s proposed discipline, not an established industry standard. Its central idea is practical: redesign the work and its controls before automating tasks across enterprise systems.
Why application modernization is not enough
Employees often bridge the gaps between applications and organizational teams. Replacing or upgrading one platform may improve that system without removing the handoffs, repeated checks, approval delays, or manual reconciliation that make the overall work slow.
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Sarmey illustrates the problem with a billing dispute: an invoice does not match delivery, and resolving it may require information from CRM, ERP, fulfillment, contracts, finance, and operations. The employee is effectively coordinating the process across those systems. An agent that merely automates one step can preserve the same fragmented workflow—and make its mistakes travel faster. As Sarmey puts it, “Automation without work redesign can turn process debt into machine-speed process debt.”
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The strategic question is therefore not simply which tasks can be automated. It is: Which application interactions should humans still have to perform, and what work should be removed, redesigned, delegated, or kept under human judgment?
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What is Enterprise Work Architecture?
Enterprise Work Architecture, as Sarmey proposes it, is a method for designing how a business outcome is produced across people, agents, applications, data, and decision rights. It treats the workflow—not the application or AI feature—as the unit of change.
That distinction matters as agents become more common in enterprise software. Gartner’s August 26, 2025 forecast, updated September 5, projected that 40% of enterprise applications would be integrated with task-specific AI agents by the end of 2026, compared with less than 5% at the time of the forecast. Gartner also forecast agent ecosystems spanning applications and business functions by 2028. These are forecasts, not measured results for 2026 or 2028. The projection signals a direction of travel, but it does not show that organizations have redesigned the work those agents will perform.
Sarmey’s proposed EWA sequence is Outcome → Work → Authority → Execution → Evidence → Economics. Each stage answers a different design question:
1. Outcome: What business result matters?
Start with the result the organization needs, expressed in terms that can be assessed: for example, resolving a billing dispute accurately and promptly. Avoid defining success as deploying an agent, increasing prompt volume, or purchasing licenses. Those describe technology activity, not whether the work improved.
2. Work: What needs to happen?
Map the tasks required to reach the outcome. Identify unnecessary steps, repeated data entry, handoffs, exception paths, and points where context is lost. Decide where human judgment is genuinely necessary rather than assuming that every existing task should be automated as written.
3. Authority: What may each participant do?
Specify whether a person, application, or agent may retrieve information, interpret it, recommend an action, prepare a transaction, approve it, execute it, or escalate it. These are materially different permissions. An agent that can retrieve an account balance does not automatically need permission to change a customer record; one that can prepare a purchase order does not automatically need authority to release it. “Capability cannot silently become authority,” Sarmey writes.
4. Execution: Which systems carry out the work?
Identify the systems, APIs, data, and workflow connections needed to complete the redesigned process. Sarmey recommends separating an engagement layer—the interfaces through which people and agents work—from systems of record, which remain important for authoritative data, transaction controls, and resilience. Fewer direct employee interactions with a system of record do not make that system dispensable.
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5. Evidence: Can consequential actions be understood and recovered?
Make significant actions observable and reconstructable: what happened, on what basis, under whose or what authority, and with what human intervention. The design should allow an organization to challenge a decision, investigate an exception, and recover from an incorrect action where possible.
6. Economics: Did the redesigned work improve?
Assess the results against the original business outcome. Sarmey proposes measures such as time-to-outcome, human touches, exceptions, rework, cost, control overhead, and business value. These are suggested management measures, not a validated universal benchmark. Their usefulness depends on defining the workflow and baseline clearly enough to compare like with like.
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How the method changes a billing-dispute workflow
Sarmey’s example is illustrative, not a reported case study or evidence of measured savings. It shows how the six stages can change the design of a process that crosses several systems:
- Define the outcome: Resolve an invoice-and-delivery mismatch accurately, with a clear record of the decision.
- Map the work: Determine which evidence is needed from CRM, ERP, fulfillment, contracts, finance, and operations; remove avoidable handoffs and duplicate checks.
- Set authority: Let machines gather and reconcile evidence, but reserve ambiguous or consequential judgment for people. Define thresholds within which an agent may act and cases it must escalate.
- Connect execution: Use governed APIs to carry out approved actions in the authoritative systems rather than treating a conversational interface as the transaction record.
- Preserve evidence: Keep a trace of the decision, the authority used, and any human review or intervention.
- Evaluate economics: Track resolution time, human touches, exceptions, rework, cost, and customer impact against the intended outcome.
The point is not that every dispute should be handled without people. It is that routine evidence gathering and reconciliation may be separated from cases requiring judgment, with explicit escalation when the facts or consequences exceed an agent’s authority.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhat controls should enterprise AI agents have?
When agents can act across application boundaries, Sarmey recommends treating them as governed enterprise participants rather than as features with implicit access. The controls should match the agent’s permitted actions and the consequences of those actions.
- Identity: Make clear which agent or service performed an action.
- Delegated permissions: Grant only the access needed for the assigned work, and distinguish read, prepare, approve, and execute permissions.
- Purpose and data boundaries: Limit what information the agent may use and the purpose for which it may use it.
- Transaction limits: Set thresholds on the actions or values an agent may process without additional approval.
- Segregation of duties: Prevent one agent or workflow from performing incompatible steps where independent review is required.
- Observability and escalation: Record consequential activity and route uncertain, exceptional, or high-impact cases to the appropriate person.
- Lifecycle controls: Govern changes to an agent’s permissions and role as it is introduced, modified, or retired.
These are Sarmey’s recommendations, not a claim that one control set fits every organization. The appropriate boundary depends on the task, data, system quality, action’s consequences, and whether the result can be reviewed and reversed. The article also refers to the NIST AI Risk Management Framework and its functions—Govern, Map, Measure, and Manage—as a governance reference.
How CIOs can decide what work to redesign first
Start with work that crosses organizational or system boundaries, rather than selecting candidates only because an application vendor has added an AI feature. Sarmey recommends involving business, operations, finance, security, risk and legal, HR, audit, and enterprise architecture in the inventory and design effort.
- Inventory the work: Identify the outcomes and workflows that matter, including the people and systems involved.
- Locate friction: Look for cross-system handoffs, exceptions, approval delays, repeated effort, and rework.
- Separate routine from consequential judgment: Determine which tasks can be removed or standardized, which could be delegated within a boundary, and which require human decision-making.
- Check execution readiness: Assess data quality, API access, system-of-record responsibilities, and the ability to observe and recover actions.
- Set measures before deployment: Choose outcome measures such as time-to-outcome, exception rate, rework, cost per governed outcome, control overhead, quality, risk, and customer or employee experience. These are analytical criteria, not a published comparative study.
This sequence helps answer the funding question: Are we paying for AI features, or redesigning how the enterprise operates? An AI capability may still be useful, but it is not itself evidence that the work has improved.
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Counting agents, prompts, or licenses can show adoption, but it cannot establish that an outcome arrived sooner, that fewer errors occurred, or that controls remained effective. A more useful evaluation connects the chosen measures to the redesigned workflow and examines trade-offs: an apparent reduction in human touches may be offset by more exceptions, rework, or control overhead.
Keep the comparison grounded in the action an agent performs. Retrieving information, recommending a response, preparing a transaction, and executing it carry different levels of authority and consequence. The quality of the underlying data and APIs, the visibility of actions, and the ability to reconstruct and recover them also affect whether an apparent efficiency gain is acceptable.
Sarmey’s argument is ultimately organizational as much as technical: map the work first, design authority deliberately, connect systems through governed execution, preserve evidence, and judge the result by business value. EWA is one proposed way to organize that effort—not a guarantee of productivity gains or a substitute for domain-specific governance.
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